Changeset 11359:8d54e79aa135 in orange for docs/widgets/rst/classify/naivebayes.rst
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 02/27/13 15:02:50 (14 months ago)
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docs/widgets/rst/classify/naivebayes.rst
r11050 r11359 21 21 22 22  Learner 23 The naive Bayesian learning algorithm with settings as specified in the dialog. 23 The naive Bayesian learning algorithm with settings as specified in 24 the dialog. 24 25 25 26  Naive Bayesian Classifier … … 27 28 28 29 29 Signal :code:`Naive Bayesian Classifier` sends data only if the learning data (signal :code:`Examples` is present. 30 Signal :code:`Naive Bayesian Classifier` sends data only if the learning 31 data (signal :code:`Examples` is present. 30 32 31 33 Description … … 34 36 This widget provides a graphical interface to the Naive Bayesian classifier. 35 37 36 As all widgets for classification, this widget provides a learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, for instance :code:`Test Learners`. Classifier is a Naive Bayesian Classifier (a subtype of a general classifier), built from the training examples on the input. If examples are not given, there is no classifier on the output. 38 As all widgets for classification, this widget provides a learner and 39 classifier on the output. Learner is a learning algorithm with settings 40 as specified by the user. It can be fed into widgets for testing learners, 41 for instance :ref:`Test Learners`. Classifier is a Naive Bayesian Classifier 42 (a subtype of a general classifier), built from the training examples on the 43 input. If examples are not given, there is no classifier on the output. 37 44 38 45 .. image:: images/NaiveBayes.png 39 46 :alt: NaiveBayes Widget 40 47 41 Learner can be given a name under which it will appear in, say, :code:`Test Learners`. The default name is "Naive Bayes". 48 Learner can be given a name under which it will appear in, say, 49 :ref:`Test Learners`. The default name is "Naive Bayes". 42 50 43 Next come the probability estimators. :obj:`Prior` sets the method used for estimating prior class probabilities from the data. You can use either :obj:`Relative frequency` or the :obj:`Laplace estimate`. :obj:`Conditional (for discrete)` sets the method for estimating conditional probabilities, besides the above two, conditional probabilities can be estimated using the :obj:`mestimate`; in this case the value of m should be given as the :obj:`Parameter for mestimate`. By setting it to :obj:`<same as above>` the classifier will use the same method as for estimating prior probabilities. 51 Next come the probability estimators. :obj:`Prior` sets the method used for 52 estimating prior class probabilities from the data. You can use either 53 :obj:`Relative frequency` or the :obj:`Laplace estimate`. 54 :obj:`Conditional (for discrete)` sets the method for estimating conditional 55 probabilities, besides the above two, conditional probabilities can be 56 estimated using the :obj:`mestimate`; in this case the value of m should be 57 given as the :obj:`Parameter for mestimate`. By setting it to 58 :obj:`<same as above>` the classifier will use the same method as for 59 estimating prior probabilities. 44 60 45 Conditional probabilities for continuous attributes are estimated using LOESS. :obj:`Size of LOESS window` sets the proportion of points in the window; higher numbers mean more smoothing. :obj:`LOESS sample points` sets the number of points in which the function is sampled. 61 Conditional probabilities for continuous attributes are estimated using 62 LOESS. :obj:`Size of LOESS window` sets the proportion of points in the 63 window; higher numbers mean more smoothing. 64 :obj:`LOESS sample points` sets the number of points in which the function 65 is sampled. 46 66 47 If the class is binary, the classification accuracy may be increased considerably by letting the learner find the optimal classification threshold (option :obj:`Adjust threshold`). The threshold is computed from the training data. If left unchecked, the usual threshold of 0.5 is used. 67 If the class is binary, the classification accuracy may be increased 68 considerably by letting the learner find the optimal classification 69 threshold (option :obj:`Adjust threshold`). The threshold is computed from 70 the training data. If left unchecked, the usual threshold of 0.5 is used. 48 71 49 When you change one or more settings, you need to push :obj:`Apply`; this will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. 72 When you change one or more settings, you need to push :obj:`Apply`; 73 this will put the new learner on the output and, if the training examples 74 are given, construct a new classifier and output it as well. 50 75 51 76 … … 53 78  54 79 55 There are two typical uses of this widget. First, you may want to induce the model and check what it looks like in a `Nomogram <Nomogram.htm>`_. 80 There are two typical uses of this widget. First, you may want to induce 81 the model and check what it looks like in a :ref:`Nomogram`. 56 82 57 83 .. image:: images/NaiveBayesSchemaClassifier.png 58 84 :alt: Naive Bayesian Classifier  Schema with a Classifier 59 85 60 The second schema compares the results of Naive Bayesian learner with another learner, a C4.5 tree. 86 The second schema compares the results of Naive Bayesian learner with 87 another learner, a C4.5 tree. 61 88 62 89 .. image:: images/C4.5SchemaLearner.png
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